Data Analytics Engineer

The State of Wisconsin Investment Board is seeking a senior individual contributor to deliver trusted analytics-ready investment data solutions across security master, entity master, reference data, pricing, holdings, and related domains. This hybrid role in Madison, WI focuses on engineering, monitoring, and governance of investment data using SQL and Python.

Key Responsibilities

• Act as a subject matter expert for security master, entity master, reference data, pricing, holdings, and related investment data.
• Interpret identifiers, classifications, instrument and issuer relationships, currencies, market conventions, corporate actions, price sources, valuation timing, and other attributes that influence investment processes.
• Define and maintain source-selection, golden-source, and match and master rules for assigned data domains.
• Trace data from external providers through ingestion, mastering, transformation, validation, and downstream consumption.
• Investigate securities, prices, identifiers, classifications, holdings, and other records that are missing, stale, duplicated, incorrectly mapped, or rejected.
• Assess the business impact of data issues and coordinate resolution across Investment Management, Operations, Risk, ETL Engineering, Technology, and external providers.
• Participate in an after-hours on-call rotation as needed for critical data delivery failures.
• Identify recurring failure patterns and implement monitoring, validation, automation, or exception-handling improvements to reduce manual intervention.
• Develop and optimize SQL and Python transformations, data models, reconciliations, validation routines, and analytics-ready datasets.
• Use Git for branching, commits, pull requests, code reviews, and merge conflict resolution.
• Apply established CI/CD pipelines to run tests, review results, promote approved changes, and validate deployments.
• Maintain analytics workflows through peer review, automated testing, controlled deployment, observability, and documentation practices.
• Evaluate technologies and AI capabilities based on the problem being solved, and learn new tools as SWIB’s data environment evolves.
• Analyze historical patterns, distributions, relationships, and time-series behavior in investment and reference data.
• Design rule-based and statistical monitors for missing, stale, unusual, or inconsistent securities, prices, holdings, classifications, and other data.
• Establish thresholds and tolerances that reflect asset-class characteristics, market conditions, source behavior, and normal variation.
• Back-test proposed controls and monitors against historical data prior to implementation.
• Evaluate false positives, false negatives, detection rates, and exception volumes, and adjust monitoring logic to improve operational usefulness.
• Assess advanced statistical or machine-learning approaches when they provide a measurable advantage over deterministic rules.
• Communicate statistical findings and automated alerts in practical business terms so results remain understandable and auditable.
• Implement preventive and detective controls for timeliness, completeness, accuracy, validity, consistency, uniqueness, and referential integrity.
• Monitor data-quality measures, investigate exceptions, perform impact analysis, and coordinate remediation.
• Lead complex initiatives by translating investment and operational needs into data models, transformation rules, validation requirements, test plans, and technical specifications.
• Identify gaps in data architecture, controls, integration patterns, and support processes, and recommend practical solutions.
• Review solution designs, data models, SQL, Python, test plans, and documentation, providing clear and actionable feedback.
• Mentor engineers in investment data, security mastering, pricing, statistical monitoring, troubleshooting, and engineering practices.
• Lead discussions with key stakeholders across multiple business functions.

Required Qualifications

• Bachelor’s degree in data analytics, data science, engineering, information systems, finance, or a related field.
• 6+ years of progressive experience in analytics engineering, investment data management, data architecture, securities operations, or a related discipline.
• Strong understanding of security and entity mastering, investment reference data, pricing, and how these data affect downstream investment processes.
• Advanced SQL skills and working proficiency in Python.
• Hands-on Git experience, including branches, commits, pull requests, code reviews, and merge conflict resolution.
• Experience with agile methodology workflow tools such as Jira.
• Experience using established CI/CD pipelines to test, promote, deploy, and validate changes (experience designing or administering CI/CD infrastructure is not required).
• Experience implementing data-quality controls, reconciliations, exception workflows, root-cause analysis, lineage, and governance practices.
• Experience with cloud data platforms such as Snowflake, Microsoft Azure, or comparable technologies.
• Ability to learn unfamiliar tools, select technology based on the problem, lead cross-functional work, and communicate with technical and investment audiences.

Preferred Qualifications

• Master’s degree in data science, statistics, financial mathematics, computer science, or another quantitative discipline.
• Experience applying statistical analysis to data-quality or operational problems, including data profiling, distribution analysis, threshold design, time-series analysis, outlier detection, or anomaly detection.
• Experience working with multiple asset classes and their reference-data and pricing conventions.
• Experience with investment platforms or data providers such as SimCorp, Markit EDM, FactSet, Bloomberg, BlackRock Aladdin, MSCI, or Charles River Development.

Technologies

• SQL
• Python
• Git
• CI/CD pipelines
• Jira
• Snowflake
• Microsoft Azure
• AI

About the Team

The Data Delivery and Operations Division partners with Investment Management, Operations, Risk, and Technology to deliver trusted, timely, and analytics-ready data.

Compensation and Benefits

• Competitive total cash compensation, based on AON (formerly McLagan) industry benchmarks.
• Comprehensive benefits package.
• Educational and training opportunities.
• Tuition reimbursement.
• Challenging work in a professional environment.
• Hybrid work environment with weekly presence in offices (frequency depends on distance from the office or the job position).
• Relocation reimbursement to the Dane County area per policy.

Location and Work Setup

Madison, WI (hybrid). Weekly office presence is required based on distance or role needs.

Role Summary

This senior individual contributor role focuses on engineering trusted analytics-ready investment data solutions, governing data quality, and building automated monitoring and troubleshooting workflows across key investment data domains.